Genuary!

Spend a month making one beautiful thing per day, given a bunch of prompts. A month late, but as they say, ’the second best time is now'.

Let’s do it!

1) Perfect loop / Infinite loop / endless GIFs

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I recently stumbled across a post that reminded me that Hugo has pipes. You can use them to automatically download files and include them as local. This seems like a pretty good idea for JS/CSS (you can argue caching versus security/locality all you want), but I’m going to give it a try.

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Last time we had Runelang: A Bind Rune Generator. This time, let’s make ‘summoning circles’. Basically, we want to make a circle with stars and other circles inscribed and around the borders with various ‘mystic’ text in the mix. Something like this:

• generate_summoning_circle
  • random chance of boder
  • random chance of one or more inscribed stars
  • random chance of recurring on the border (calling generate_summoning_circle again)
  • random chance of recurring in the middle

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Continuing with my Runelang in the Browser series, I had the idea to automatically generate runes. So basically reversing the parsing step, rather than to take what I’ve written and make it look good, to write something that Runelang can parse–and still look good.

In a nutshell, I want to write a series of functions that can recursively call one another to render runes:

• generate_bind_rune
  • n times generate_bind_rune_arm
    • m times generate bars, circles, and other decrations
    • add a fork at the end

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As they say, life is what happens when you’re making other plans. But I’m back, so let’s talk some more about Runelang. In the interest of not dragging on months without finishing, we’re going to go ahead and push through the rest of the project. Onward!

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Earlier this week, we started parsing, getting through groups, nodes, params, and lists. A pretty good start, but it also leaves out two very powerful things (expressions and defines), one of which we absolutely do need to start actually evaluating things: expressions. Since we use them in every param, we pretty much need to know how to parse them, so let’s do it!

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I’m still here! And less sick now.

Last time(s), we described and lexed) Runelang! This time around, let’s take the lexed tokens and go one step further and parse them!

So, how do we go about this? With a recursive descent parser!

• Start with a list/stream of tokens
• Using the first k (in a LL(k) parser) elements of the list, identify which sort of object we are parsing (a group / identifier / literal / expression / etc)
• Call a parsing function for that object type (parseGroup etc) that will:
  • Recursively parse the given object type (this may in turn call more parse functions)
  • Advance the token stream ‘consuming’ any tokens used in this group so the new ‘first’ element is the next object

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Let’s LEX!

So this is actually one of the easier parts of a programming language. In this case, we need to turn the raw text of a program into a sequence of tokens / lexemes that will be easier to parse. In this case, we want to:

• Remove all whitespace and comments
• Store the row and column with the token to make debugging easier

So let’s do it!

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Previously, I wrote a post about making a DSL in Ruby that could render magic circles/runes. It worked pretty well. I could turn things like this:

rune do
    scale 0.9 do 
        circle
        polygon 7
        star 14, 3
        star 7, 2
        children 7, scale: 1/8r, offset: 1 do |i|
            circle
            invert do
                text (0x2641 + i).chr Encoding::UTF_8
            end
        end
    end
    scale 0.15 do
        translate x: -2 do circle; moon 0.45 end
        circle
        translate x: 2 do circle; moon 0.55 end
    end
end

Into this:

But… I decided to completely rewrite it. Now it’s an entirely separate language:

Output

Source

Log (most recent messages first):

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Okay. A random post on the /r/cellular_automata subreddit inspired me.

Let’s generate a cellular automata where each pixel updates based on a neural network given as input:

• The x/y coordinates (scaled to the range 0-1)
• An optional random value (to make it more dynamic)
• A variety of neighboring data, such as:
  • The number of neighbors that are ‘active’ (> 50% white), ranges 0-8 scaled to 0-1. This should allow Conway's Game of Life
  • The RGB values of all neighbors (allows a superset of the above)
  • Gradients, subtract color value of the left from the right so that you get edges and side to side movement

Let’s do it!

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